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Opportunity-to-Learn Index×Educational Production Function×
DziedzinaEducationEducation
RodzinaProcess / pipelineRegression model
Rok powstania19951979
TwórcaCarroll (concept); Husén/IEA (measurement); McDonnell; Schmidt (TIMSS)Economics of education (Coleman; Hanushek; Todd & Wolpin)
TypQuantitative index of students' exposure to instructional content and resourcesRegression relating educational inputs to achievement outputs
Źródło pierwotneMcDonnell, L. M. (1995). Opportunity to learn as a research concept and a policy instrument. Educational Evaluation and Policy Analysis, 17(3), 305–322. DOI ↗Hanushek, E. A. (1979). Conceptual and empirical issues in the estimation of educational production functions. Journal of Human Resources, 14(3), 351–388. DOI ↗
Inne nazwyOTL Index, Content Coverage Index, Curriculum Exposure Measure, Opportunity-to-Learn MeasurementEducation Production Function, Schooling Production Function, Input-Output Model of Education, Achievement Production Function
Pokrewne43
PodsumowanieAn opportunity-to-learn (OTL) index quantifies how much exposure students have had to the content and instructional resources they need to succeed on an assessment. Rooted in Carroll's model of school learning and developed through the IEA international studies, OTL measurement asks whether students were actually taught the material before being tested on it. Constructed from teacher reports, curriculum analysis, or instructional logs, OTL indices are used both as a fairness criterion for interpreting test scores and as a policy instrument for monitoring equitable access to the intended curriculum.The educational production function is the economist's framework for relating the inputs of schooling — class size, teacher quality, expenditure, family background — to an output, usually measured achievement. Borrowing the production-function metaphor from the economics of the firm, it estimates by how much achievement changes when an input changes. It is the analytic backbone of decades of debate over what resources matter for learning, and the methodological challenges of estimating it honestly — endogeneity, omitted variables, and the cumulative history of inputs — define much of the field.
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